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Department of Networking and Communications, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India
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Department of Networking and Communications, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India
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Department of Networking and Communications, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India
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Department of Electronics and Communication Engineering, School of Engineering and Technology, St. Joseph University, Chennai, Tamil Nadu, India
Department of Networking and Communications, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India
Classification of crops and weeds is an important aspect in precision agriculture, as it aids in providing a means for efficient weed control and reduced herbicide applications to achieve high crop productivity. In this paper, a quantum-classical hybrid deep learning approach is presented for accurate identification of crops and weeds from sorghum images using the dataset Sorghum Crop Weed Dataset_Classification. In this hybrid framework, a convolutional neural network (CNN) for feature extraction in the spatial domain, along with a variational quantum circuit (VQC) to provide better feature representation and classification abilities in higher-dimensional quantum space, is used. This allows for effective differentiation of crop and weed instances that appear alike in different agricultural conditions. Data preprocessing methods like resizing, normalization, and data augmentation (rotation, flipping, and brightness adjustment) are performed to ensure uniformity in data and increase the generalizability of the trained model. According to experimental evaluations conducted using traditional metrics, the designed model attains an accuracy of 97.3%, precision of 96.9%, recall of 97.2%, F1-score of 97.1%, and a training loss of 0.09. The results show a significant increase from baseline approaches such as CNN (92.5% accuracy), ResNet (94.0% accuracy), and transfer learning (95.0% accuracy). In addition, the designed model performs well when tested under challenging situations encountered in the real-world agricultural environment, such as varying illuminations, partial occlusion, and background noises. In summary, the above results prove the success of combining classical deep learning and quantum feature learning to enhance classification performance.
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